Analysis method and devices for same

ABSTRACT

In order to provide a method for anomaly and/or fault recognition in an industrial-method plant, for example a painting plant, wherein anomalies and/or fault situations are recognisable simply and reliably by means of the method, it is proposed according to the invention that the method should comprise the following:
         automatic generation of an anomaly and/or fault model of the industrial-method plant that comprises information on the occurrence probability of process values;   automatic input of process values of the industrial-method plant during operation thereof;   automatic recognition of an anomaly and/or fault situation by determining an occurrence probability by means of the anomaly and/or fault model on the basis of the process values of the industrial-method plant that have been input and by checking the occurrence probability for a limit value,

RELATED APPLICATIONS

This application is a national phase of international application No.PCT/DE2020/100358, filed on Apr. 29, 2020, and claims the benefit ofGerman application No. 10 2019 112 099.3, filed on May 9, 2019, andGerman application No. 10 2019 206 837.5, filed on May 10, 2019, all ofwhich are incorporated herein by reference in their entireties and forall purposes.

FIELD OF DISCLOSURE and BACKGROUND

The disclosure relates to a method for fault analysis in anindustrial-method plant, for example a painting plant.

SUMMARY

An object of the disclosure is to provide a method for fault analysis inan industrial-method plant, for example a painting plant, by means ofwhich fault situations are analysable simply and reliably.

This object is achieved by a method for fault analysis in anindustrial-method plant, for example a painting plant.

The method for fault analysis in an industrial-method plant, for examplea painting plant, preferably comprises the following:

-   -   in particular automatic recognition of a fault situation in the        industrial-method plant;    -   storage of a fault situation data set for the respective        recognised fault situation, in a fault database;    -   automatic determination of a cause of the fault for the fault        situation and/or automatic determination of process values that        are relevant to the fault situation, on the basis of the fault        data set of a respective recognised fault situation.

In the context of this description and the attached claims, the term“process values causing the fault situation” is understood in particularto mean process values that cause the fault situation and/or are relatedthereto.

In the context of this description and the attached claims, the term “inparticular” is used exclusively to describe possible discretionaryand/or optional features.

It may be favourable if the fault situation is recognised automaticallyby means of a message system.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided, for the purpose ofautomatically determining the fault cause for the fault situation and/orautomatically determining the process values relevant to the faultsituation, for one or more process values to be automatically linked tothe fault situation on the basis of one or more of the following linkcriteria:

-   -   prior linking from a message system;    -   an association of a process value with the same part of the        industrial-method plant as that in which the fault situation        occurred;    -   linking a process value to a historical fault situation on the        basis of active selection by a user;    -   an active selection of the process value by a user.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided, for the purpose ofautomatically determining the fault cause for the fault situation and/orautomatically determining the process values relevant to the faultsituation, for automatic prioritisation of the process values linked tothe fault situation to be carried out automatically on the basis of oneor more of the following prioritisation criteria:

-   -   a process relevance of the process values;    -   a position of a process value or of a sensor determining the        process value within the industrial-method plant;    -   an amount by which a process value deviates from a defined        process window and/or a normal condition;    -   a prioritisation of historical process values in historical        fault situations; by adopting a prioritisation of the fault        cause and/or the process values from a message system;    -   a prioritisation by a user.

Prioritisation based on the process relevance of the process values ispreferably performed such that process-critical process values are givenhigher priority.

In the context of this description and the attached claims, the term“process-critical process value” is understood in particular to mean aprocess value that is stored as process-critical in the message systemand/or has been defined as process-critical by a user.

A prioritisation based on a position of the process value or of a sensordetermining the process value within the industrial-method plant ispreferably performed such that process values that are associated withthe same plant part, a nearby plant part and/or a comparable plant partare given higher priority.

In the context of this description and the attached claims, the term“comparable plant parts” is understood in particular to mean plant partsof similar or identical layout.

Comparable plant parts are for example industrial supply air plant ofthe same or similar construction, conditioning modules of an industrialsupply air plant that have the same or similar construction, or pumps ormotors of the same or similar construction.

A position of the sensor that determines the process value is preferablyidentified using a classification comprising a numbering system (theso-called plant numbering system) in the industrial-method plant.

Process values are designated unambiguously, preferably by means of thenumbering system.

Preferably, process values are prioritised in dependence on theirdesignation in the numbering system.

For the purpose of unambiguous designation of sensors and/or processvalues, the numbering system preferably comprises the designation of afunctional unit, the designation of a functional group of the respectivefunctional unit and/or the designation of a functional element of therespective functional group with which the respective sensor and/orprocess value is associated.

Further, it may be favourable if the unambiguous designation of aprocess value by means of the numbering system comprises a designationof a type of measured variable, for example temperature, throughflow,pressure.

For example, a supply air plant of a painting plant is a functionalunit, wherein a conditioning module of the supply air plant is afunctional group and a pump of the supply air plant is a functionalelement.

A normal condition of a process value is preferably determined by meansof a method for anomaly and/or fault recognition.

A prioritisation based on prioritising historical process values inhistorical fault situations is preferably performed such that processvalues are prioritised analogously with the historical fault situation.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided, for the purpose ofautomatically determining the fault cause for the fault situation and/orautomatically determining the process values relevant to the faultsituation, for further fault causes and/or process values to beproposed, wherein the proposal is made automatically on the basis of oneor more of the following proposal criteria:

-   -   a process relevance of the process values;    -   a position of a process value or of a sensor determining the        process value within the industrial-method plant;    -   an amount by which a process value deviates from a defined        process window and/or a normal condition;    -   a prioritisation of historical process values in historical        fault situations; physical dependences of the process values.

It is preferable for a process-critical process value to be preferablyproposed.

A proposal based on a position within the industrial-method plant of theprocess value or of a sensor that determines the process value ispreferably made such that process values that are associated with thesame plant part, a nearby plant part and/or a comparable plant part areproposed by preference.

Preferably, process values are proposed in dependence on theirdesignation in the numbering system.

A proposal based on a prioritisation of historical process values inhistorical fault situations is preferably made such that process valuesof high priority in the historical fault situation are preferablyproposed.

For the purpose of determining a proposal based on physical dependencesof the process values, the physical dependences are preferably definedby a user as an expert rule.

Preferably, a prioritisation of the proposed fault causes and/or processvalues is modifiable by a user.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for historical fault situationsto be determined from a fault database using one or more of thefollowing similarity criteria:

-   -   a fault classification of the historical fault situation;    -   a historical fault situation in the same or a comparable plant        part;    -   process values of the historical fault situation that are        identical or similar to process values of the recognised fault        situation.

It is preferable for historical fault situations having a faultclassification that is identical to the recognised fault situation to bepreferably determined.

The fact that the process values of the historical fault situation areidentical or similar to the process values of the recognised faultsituation is preferably determined by a comparison algorithm.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for historical process valuesthat are identical or similar to process values of the recognised faultsituation to be determined from a process database.

Preferably, for the purpose of determining the historical process valuesa process database is searched. It may be favourable if the fact thatthe process values are identical or similar is determined by means of acomparison algorithm.

Preferably, determining the historical process values is performedautomatically.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for the determined historicalprocess values to be characterised as belonging to a historical faultsituation.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided, for a recognised faultsituation, for a fault situation data set to be stored in a faultdatabase.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for a respective faultidentification data set to comprise one or more of the following faultsituation data:

-   -   a fault classification of the fault situation;    -   process values that are linked to the fault situation, based on        a prior linking from a message system;    -   information on a point in time at which a respective fault        situation occurred;    -   information on a duration for which a respective fault situation        occurred;    -   information on the location in which a respective fault        situation occurred;    -   alarms;    -   status messages.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for the fault situation data setof a respective fault situation to comprise fault identification datafor unambiguous identification of the recognised fault situation.

Preferably, the fault identification data are usable for unambiguousdesignation of a fault situation.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for documentation data and faultelimination data to be stored in the fault situation data set of arespective fault situation.

Documentation data preferably comprise operating instructions, manuals,circuit diagrams, procedure diagrams and/or data sheets of the plantparts that are affected by a respective fault situation.

Fault elimination data preferably comprise information on theelimination of a fault situation, in particular procedural instructionsfor eliminating a fault situation.

In particular, documentation data and fault elimination data are alsoappendable to the fault situation data set by a user.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for process values to be storedduring operation of the industrial-method plant, synchronised with arecognised fault situation.

In one embodiment of the method for fault analysis in anindustrial-method plant, it is provided for process values to beprovided with a time stamp by means of which the process values areconfigured to be unambiguously associated with a point in time.

Further, the disclosure relates to a fault analysis system for faultanalysis in an industrial-method plant, for example a painting plant,wherein the system takes a form and is constructed for the purpose ofcarrying out the method according to examples disclosed herein for faultanalysis in an industrial-method plant, for example a painting plant.

Further, the disclosure relates to an industrial control system thatcomprises a fault analysis system according to examples disclosedherein.

Further, the disclosure relates to a method for predicting processdeviations in an industrial-method plant, for example a painting plant.

A further object of the disclosure is to provide a method for predictingprocess deviations in an industrial-method plant, for example a paintingplant, by means of which process deviations are predictable simply andreliably.

This object is achieved by a method for predicting process deviations inan industrial-method plant, for example a painting plant.

The method for predicting process deviations in an industrial-methodplant, for example a painting plant, preferably comprises the following:

-   -   automatic generation of a prediction model;    -   prediction of process deviations during operation of the        industrial-method plant, using the prediction model.

Preferably, a process deviation of production-critical process values ispredictable by means of the prediction model.

In one embodiment of the method for predicting process deviations, it isprovided for the method for predicting process deviations to be carriedout in an industrial supply air plant, a pre-treatment station, astation for cathodic dip coating and/or a drying station.

Industrial supply air plants, pre-treatment stations and/or stations forcathodic dip coating are in particular very sluggish industrial-methodplants.

Consequently, production-critical process values of industrial-methodplants of this kind change only very slowly during operation thereof.

Because of the high inertia of industrial-method plants of this kind, aprocess deviation during operation of the industrial-method plant ispreferably predictable at an early stage by means of the predictionmodel.

Preferably, it is thus possible to achieve a time gain for repair and/ormaintenance of industrial-method plants of this kind before a processdeviation occurs.

An industrial supply air plant preferably comprises a plurality ofconditioning modules, for example a pre-heating module, a coolingmodule, a post-heating module and/or a wetting module.

Preferably, the prediction model that is generated is transferable tosimilar industrial-method plants.

For example, it is conceivable for a prediction model that was generatedfor a pre-treatment station to be usable for a station for cathodic dipcoating.

In one embodiment of the method for predicting process deviations, it isprovided for process deviations of production-critical process values inthe industrial-method plant to be predicted by means of the predictionmodel, in particular on the basis of changing process values duringoperation of the industrial-method plant.

In the context of this description and the attached claims, the term“production-critical process values” is understood in particular to meanprocess values of which the deviation from a predetermined processwindow results in a deviation in quality, in particular deficiencies inquality.

Production-critical process values of an industrial supply air plant arefor example the temperature and relative air humidity of the airconditioned by means of the industrial supply air plant, in particularat an exhaust part of the industrial supply air plant.

In a painting plant, air that is conditioned by means of an industrialsupply air plant is preferably fed to a painting booth, and thuspreferably acts directly on a treatment quality of the workpiecestreated in the painting booth, in particular the vehicle bodies treatedin the painting booth.

For example, it is possible to use the prediction model to predictprocess deviations of production-critical process values for aprediction horizon of at least approximately 10 minutes, for example atleast approximately 15 minutes, preferably at least approximately 20minutes.

In one embodiment of the method for predicting process deviations, it isprovided, for the purpose of automatically generating the predictionmodel, to store process values and/or status variables during operationof the industrial-method plant for a predetermined period.

If the industrial-method plant is an industrial supply air plant, thestored process values and/or status variables preferably comprise thefollowing:

-   -   target variables of the industrial supply air plant, in        particular temperature and relative air humidity of the air        conditioned by means of the industrial supply air plant, in        particular at an exhaust part of the industrial supply air        plant;    -   control variables, in particular valve positions of valves of        heating and/or cooling modules of the industrial supply air        plant, rotational frequencies of pumps, in particular the        wetting pump, and/or rotational frequencies of ventilators;    -   internal variables, in particular supply and/or return flow        temperatures in the heating and/or cooling modules of the        industrial supply air plant and/or air conditions between        conditioning modules;    -   measured disruption variables, in particular external        temperature and/or external relative air humidity at an intake        part of the industrial supply air plant;    -   unmeasured disruption variables; and/or    -   status variables, in particular wetting pump (on/off), manual        mode for pumps (on/off), feed valves (open/closed), ventilator        (on/off).

In the context of this description and the attached claims, the term“process values” is understood in particular to mean continuoustime-dependent signals.

In the context of this description and the attached claims, the term“status variables” is understood in particular to mean discretetime-dependent events.

In one embodiment of the method for predicting process deviations, it isprovided for the predetermined period for which process values and/orstatus variables are stored during operation of the industrial-methodplant to be predetermined in dependence on one or more of the followingcriteria:

-   -   the industrial-method plant is in an operation-ready state, in        particular for a production operation, for at least        approximately 60%, preferably for at least approximately 80%, of        the predetermined period;    -   the industrial-method plant is in a production-ready state for        at least approximately 60%, preferably for at least        approximately 80%, of the predetermined period;    -   during the predetermined period, the industrial-method plant is        operated in particular using all possible operating strategies;    -   a predetermined number of process deviations and/or disruptions        in the predetermined period.

If the industrial-method plant is an industrial supply air plant, it ispreferably in an operation-ready state for a production operation if:

-   -   a ventilator of the industrial supply air plant is in operation        (status variable of the ventilator is “on”);    -   conditioning modules of the industrial supply air plant are        operated in an automatic mode;    -   at least one control valve is open; and/or    -   a wetting pump is in operation (status variable of the wetting        pump is “on”).

In the context of this description and the attached claims, the term“production-ready state of an industrial-method plant” is understood inparticular to mean that target variables of the industrial-method plantare within a predetermined process window.

If the industrial-method plant is an industrial supply air plant, it isin a production-ready state if the target variables of the industrialsupply air plant, in particular temperature and relative air humidity ofthe air conditioned by means of the industrial supply air plant, inparticular at an exhaust part of the industrial supply air plant, arewithin a predetermined process window.

A pre-treatment station or a station for cathodic dip coating are inparticular operable only with a single operating strategy.

An industrial supply air plant is operable in particular with aplurality of operating strategies, in particular in dependence onambient conditions.

An industrial supply air plant is operable for example with thefollowing operating strategies: heating/wetting, cooling/heating,cooling/wetting, cooling, heating, wetting.

If the industrial-method plant is an industrial supply air plant, it isin particular conceivable for process values and/or status variables forautomatically generating the prediction model to be stored for a periodof for example at least approximately 6 months, in particular for aperiod of at least approximately 9 months, preferably for a period of atleast approximately 12 months.

If the industrial-method plant is a pre-treatment station or a stationfor cathodic dip coating, it is in particular conceivable for processvalues and/or status variables for automatically generating theprediction model to be stored for a period of for example at leastapproximately 2 weeks, in particular for a period of at leastapproximately 4 weeks, preferably for a period of at least approximately6 weeks.

For example, it is conceivable for at least approximately 30, preferablyat least approximately 50, process deviations and/or disruptions tooccur in the predetermined period.

It is in particular also conceivable for the predetermined period duringwhich process values and/or status variables are stored during operationof the industrial-method plant to comprise a plurality of non-contiguoussub-periods.

If the period during which process values and/or status variables arestored during operation of the industrial-method plant comprises aplurality of non-contiguous sub-periods, then the sub-periods preferablyeach have one or more of the following criteria:

-   -   a minimum duration of the sub-period, for example at least        approximately 30 minutes;    -   operation of the industrial-method plant in a production-ready        and/or already operating state at the start of the sub-period;    -   operation of the industrial-method plant in an already operating        state at the end of a sub-period.

In one embodiment of the method for predicting process deviations, it isprovided, for the purpose of generating the prediction model, for amachine learning method to be carried out, wherein the process valuesand/or status variables that are stored for the predetermined period areused for generating the prediction model.

Machine learning methods that are carried out for the purpose ofautomatically generating the prediction model preferably comprise one ormore of the following: gradient boosting, a random forest, a supportvector machine.

In one embodiment of the method for predicting process deviations, it isprovided for the machine learning method to be carried out on the basisof features that are extracted from the process values and/or statusvariables stored for the predetermined period.

In one embodiment of the method for predicting process deviations, it isprovided for one or more of the following to be used for the purpose ofextracting features:

-   -   statistical key figures;    -   coefficients from a principal component analysis;    -   linear regression coefficients;    -   dominant frequencies and/or amplitudes from the Fourier        spectrum.

Statistical key figures comprise for example a minimum, a maximum, amedian, an average and/or a standard deviation.

In one embodiment of the method for predicting process deviations, it isprovided for a selected number of prediction data sets with processdeviations and a selected number of prediction data sets with no processdeviations to be used for training the prediction model.

In particular, it is conceivable for the selected number of predictiondata sets with process deviations to correspond at least approximatelyto the selected number of prediction data sets with no processdeviations.

In particular, it is conceivable for the selected number of predictiondata sets with process deviations and the selected number of predictiondata sets with no process deviations to be identical.

In one embodiment of the method for predicting process deviations, it isprovided for selection of the number of prediction data sets with aprocess deviation to be made on the basis of one or more of thefollowing criteria:

-   -   a minimum time interval between two prediction data sets with        process deviations;    -   an automatic selection on the basis of defined rules;    -   a selection by a user.

A minimum time interval between two prediction data sets with processdeviations is for example at least approximately two hours.

In one embodiment of the method for predicting process deviations, it isprovided for prediction data sets with process deviations to becharacterised as such if a process deviation occurs within apredetermined time interval.

A predetermined time interval preferably comprises a timespan of theprediction data set and a selected prediction horizon.

For example, it is conceivable for the timespan of the prediction dataset to be 30 minutes and for the selected prediction horizon to be 15minutes.

A prediction data set with no process deviations is characterised assuch if no process deviations are present within the predetermined timeinterval.

In one embodiment of the method for predicting process deviations, it isprovided for the process values and/or status variables that are storedfor the predetermined period to be grouped into prediction data sets bypre-processing.

In one embodiment of the method for predicting process deviations, it isprovided for the pre-processing to comprise the following:

-   -   regularisation of the process values stored for the        predetermined period;    -   grouping the process values and/or status variables into        prediction data sets by classifying the process values and/or        status variables into time windows with a time offset.

Preferably, the duration of a time window is greater than the timeoffset.

The duration of a time window is for example 30 minutes.

The time offset is for example 5 minutes.

Preferably, prediction data sets that succeed one another in time hereeach comprise process values and/or status variables with a timeoverlap, for example of 5 minutes.

Further, the disclosure relates to a prediction system for predictingprocess deviations in an industrial-method plant, wherein the predictionsystem takes a form and is constructed for the purpose of carrying outthe method according to examples disclosed herein for predicting processdeviations in an industrial-method plant, for example a painting plant.

Further, the disclosure relates to an industrial control system thatcomprises a prediction system according to examples disclosed herein.

The method according to examples disclosed herein for predicting processdeviations preferably has individual or a plurality of the featuresand/or advantages described in conjunction with the method according toexamples disclosed herein for fault analysis.

Further, the method according to examples disclosed herein for faultanalysis preferably has individual or a plurality of the features and/oradvantages described in conjunction with the method according toexamples disclosed herein for predicting process deviations.

Further, the disclosure relates to a method for anomaly and/or faultrecognition in an industrial-method plant, for example a painting plant.

The disclosure has the further object of providing a method for anomalyand/or fault recognition in an industrial-method plant, for example apainting plant, wherein anomalies and/or fault situations arerecognisable simply and reliably by means of the method.

This object is achieved by a method for anomaly and/or fault recognitionin an industrial-method plant, for example a painting plant.

The method for anomaly and/or fault recognition in an industrial-methodplant, for example a painting plant, preferably comprises the following:

-   -   automatic generation of an anomaly and/or fault model of the        industrial-method plant that comprises information on the        occurrence probability of process values;    -   automatic input of process values of the industrial-method plant        during operation thereof;    -   automatic recognition of an anomaly and/or fault situation by        determining an occurrence probability by means of the anomaly        and/or fault model on the basis of the process values of the        industrial-method plant that have been input and by checking the        occurrence probability for a limit value.

Preferably, fault situations, that is to say defects and/or failures incomponents, sensors and/or actuators, are identifiable by means of themethod for anomaly and/or fault recognition.

Preferably, a normal condition of the industrial-method plant isdeterminable in an automated manner by the method for anomaly and/orfault recognition in an industrial-method plant.

In particular, static and/or dynamic relationships in theindustrial-method plant are describable by means of the anomaly and/orfault model.

In the context of this description and the attached claims, the term“anomaly” is understood in particular to mean a deviation of a processvalue from a normal condition.

Preferably, the anomaly and/or fault model comprises a structure graph.

In particular, the structure graph comprises a plurality of cliques,wherein relationships between nodes of a respective clique arepreferably described by a probability density function.

Relationships in respect of sensors and/or actuators of theindustrial-method plant are preferably described by means of arespective clique of the structure graph.

Preferably, an anomaly is recognised if the occurrence probability of aprocess value in a clique of a structure graph of the anomaly and/orfault model falls below a limit value.

It may be favourable if a recognised anomaly with anomalous processvariables is displayed graphically to a user.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for

-   -   the anomaly and/or fault model to comprise structural data        containing information on a process structure in the        industrial-method plant, and/or for    -   the anomaly and/or fault model to comprise parameterisation data        containing information on relationships between process values        of the industrial-method plant.

The structural data in particular comprise information on relationshipsbetween sensors and/or actuators in the industrial-method plant.

The parameterisation data in particular comprise information on theoccurrence probability of process values.

In particular, structural data and/or parameterisation data are utilisedfor generating the anomaly and/or fault model.

In one embodiment of the method for anomaly and/or fault recognition, itis provided, for the purpose of generating the anomaly and/or faultmodel, for one or more of the following steps to be carried out:

-   -   structure identification for determining a process structure of        the industrial-method plant;    -   determination of causalities in the determined process structure        of the industrial-method plant;    -   structure parameterisation of the relationships in the        determined process structure of the industrial-method plant.

The anomaly and/or fault model preferably comprises structureinformation, causality information and/or structure parameterisationinformation.

Preferably, the structure identification is configured to facilitatestructure parameterisation.

In particular, the structure identification is configured to reduceparameterisation work and thus in particular processing work for thestructure parameterisation.

In one embodiment of the method for anomaly and/or fault recognition, itis provided, in the context of structure identification for determininga process structure of the industrial-method plant, for a structuregraph that in particular maps relationships in the industrial-methodplant to be determined.

Preferably, the structure graph comprises a plurality of nodes and aplurality of edges connecting the nodes to one another in pairs.

Preferably, the structure graph comprises a plurality of cliques.

It may be favourable if relationships in the determined structure graphare determined by means of the structure identification.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for determination of the structure graph to be performedusing one or more of the following:

-   -   a machine learning method;    -   expert knowledge;    -   known circuit diagrams and/or procedure diagrams;    -   designations in a numbering system of the industrial-method        plant.

It may be favourable if, for the purpose of structure identification, inparticular for determining the structure graph, a classificationcomprising a numbering system (the so-called plant numbering system) isused in the industrial-method plant, for example by means of a semanticanalysis.

The numbering system in particular comprises information on a functionalunit, for example on the plant type of an industrial-method plant,information on a functional group of the respective functional unit,information on a functional element of the respective functional group,and/or information on a data type.

Preferably, the numbering system comprises a plurality of levels.

A first level of the numbering system comprises for example informationon a respective functional unit.

A second level of the numbering system comprises for example informationon a respective functional group.

A third level of the numbering system comprises for example informationon a respective functional element.

A fourth level of the numbering system comprises for example informationon a respective data type.

Preferably, a numbering system data set comprises unambiguousdesignations of the functional elements of the industrial-method plant.

For example, an unambiguous designation of a functional elementcomprises information on the first, second, third and/or fourth level.

Preferably, in the semantic analysis information is extracted from thenumbering system data set, for example on the basis of unambiguousdesignations of the functional elements of the industrial-method plant.

Preferably, in the semantic analysis one or more searches of strings ina numbering system data set are carried out.

In particular, it may be provided for information to be extracted from anumbering system data set during this.

During the extraction of information, in particular a first stringsearch in the numbering system data set is carried out, wherein inparticular an extracted data set is obtained.

Information extracted from the numbering system data set is preferablycategorised for semantic analysis.

In the categorisation, for example a second string search is carriedout, in the extracted data set obtained during extraction of theinformation.

For example, it is conceivable for the particular physical variablemeasured by the sensor element to be identifiable during the semanticanalysis, in particular with one or more string searches.

Physical variables that are identifiable by means of semantic analysisare for example the following: thermodynamic variables (temperatureand/or humidity); hydraulic variables (pressure, volume and/or fillinglevel); mechanical variables (speed of rotation, torque and/orrotational position); electrical variables (frequency, voltage, currentstrength and/or electrical output).

Further, it may be favourable if status variables are identifiableduring the semantic analysis, in particular with one or more stringsearches.

Status variables that are identifiable during the semantic analysiscomprise for example the following information: information on anoperating state of a wetting pump (on/off); information on a manual modefor pumps (on/off); information on an opening status of a feed valve(open/closed); information on an operating state of a ventilator(on/off).

Preferably, determining the structure graph by means of a machinelearning method is performed using correlation coefficients by means ofwhich non-linear relationships are reproducible, for example by means ofmutual information.

In the context of this description and the attached claims, the term“expert knowledge” is understood for example to mean knowledge ofrelationships between sensors in the process.

Preferably, the configuration is such that edges between nodes of thestructure graph can be eliminated by a pre-configuration of thestructure graph, by means of information from expert knowledge, knowncircuit diagrams and/or procedure diagrams. In particular here,processing work for determining the structure graph is reducible.

Process values are preferably designated unambiguously by means of thenumbering system (“plant numbering system”).

For this reason, it may be favourable if the structure graph isdetermined using the respectively unambiguous designation of the processvalues.

In particular, it is conceivable for the structure graph determined bymeans of a machine learning method to be checked for plausibility bymeans of expert knowledge, known circuit diagrams and/or procedurediagrams and/or the designations in the numbering system of theindustrial-method plant.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for the industrial-method plant to be activated by testsignals for the purpose of structure identification, in particular fordetermining the structure graph.

Preferably, during the activation by test signals, anomalies and/orfault situations are generated deliberately.

Test signals are in particular generated taking into account technicaldata. In particular, limits for the test signals are predeterminable onthe basis of the technical data; for example, when predetermining jumpfunctions, a maximum amplitude is predeterminable for the controlvariable jumps.

In the context of this description and the attached claims, the term“technical data” is understood in particular to mean one or more of thefollowing items of information:

-   -   sensor type (temperature sensor, throughflow sensor, valve        position sensor, pressure sensor, etc.) and/or actuator type        (valve, ventilator, damper, electric motor);    -   permissible value ranges of sensors and/or actuators;    -   signal type of sensor and/or actuator (float, integer).

In particular, the industrial-method plant is activated dynamically bymeans of the test signals.

The test signals are in particular signals by means of which controlvariables in the industrial-method plant are modifiable. For example,control variables of valves and/or pumps of the industrial-method plantare modified by means of the test signals.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for the determining of causalities in the determined processstructure of the industrial-method plant to be performed using one ormore of the following:

-   -   system input signals and system output signals that are        generated on the activation of the industrial-method plant by        test signals;    -   expert knowledge;    -   known circuit diagrams and/or procedure diagrams;    -   designations in a numbering system of the industrial-method        plant.

Causalities in the determined process structure are derived for examplefrom system input signals and system output signals of theindustrial-method plant that are determined during activation of theindustrial-method plant by test signals, for example by way of therespective temporal course of the system input signals and system outputsignals.

As an alternative or in addition, it is conceivable for causalities tobe derived from system input signals and system output signals that aredetermined during activation of the industrial-method plant by testsignals, by means of causal inference methods.

In the context of this description and the attached claims, the term“causalities” is understood in particular to mean directions ofcausality, that is to say directions marked by arrows, in the determinedstructure graph.

Preferably, the process values that cause a recognised anomaly arelocatable by means of the causalities determined in the determinedprocess structure or in the determined structure graph.

In one embodiment of the method for anomaly and/or fault recognition, itis provided, for the purpose of structure parameterisation of therelationships in the determined process structure of theindustrial-method plant, for one or more of the following to be used:

-   -   methods for determining probability density functions, in        particular Gaussian mixture models;    -   known physical relationships between process values;    -   physical characteristic diagrams of functional elements of the        industrial-method plant, for example characteristic diagrams of        valves.

Preferably, the structure parameterisation is performed using methodsfor determining probability density functions, in particular usingGaussian mixture models.

It may be favourable if relationships between two variables of thefunctional element are describable by physical characteristic diagramsof functional elements of the industrial-method plant.

For example, a relationship between a valve position and a volumetricflow rate is describable by a known valve characteristic diagram of avalve.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for data from regular operation of the industrial-methodplant and/or data obtained by activation of the industrial-method plantby test signals to be used for the purpose of structure parameterisationusing methods for determining probability density functions, inparticular using Gaussian mixture models.

For example, control, measurement and/or regulating variables that arestored in particular in a database are used for the purpose of structureparameterisation using methods for determining probability densityfunctions.

Preferably, for the purpose of structure parameterisation using methodsfor determining probability density functions, data from ongoingoperation of the industrial-method plant are used, and these are storedfor a period of at least 2 weeks, preferably at least 4 weeks, forexample at least 8 weeks.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for the data that are used for structure parameterisationusing methods for determining probability density functions, inparticular using Gaussian mixture models, to be pre-processed before thestructure parameterisation.

During the pre-processing, preferably data from regular operation of theplant that are not associated with operation-ready or production-readyoperating states of the industrial-method plant (for example plantswitched off, maintenance phases, etc.) are eliminated by way of alarmsand status bits that describe the state of the industrial-method plant.

Further, it may be favourable if data from regular operation of theplant are pre-processed by filtering, for example by means of low-passfilters and/or Butterworth filters.

Preferably, data from regular operation are interpolated at a consistenttime interval.

In one embodiment of the method for anomaly and/or fault recognition, itis provided during generation of the anomaly and/or fault model for alimit value for the occurrence probability of a process value to beestablished, wherein an anomaly is recognised if this falls below thelimit value.

The limit value is preferably established in automated manner.

The limit value is preferably established by means of a non-linearoptimisation method, for example by means of the Nelder-Mead method.

As an alternative or in addition, it is conceivable to establish thelimit value by means of quantiles.

Limit values for the occurrence probability of the process values arepreferably optimisable, for example by predetermining a false-positiverate.

Preferably, after the first generation of the anomaly and/or faultmodel, the limit values are adapted, in particular in the event of toohigh a number of false alarms.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for a fault cause of a recognised anomaly and/or arecognised fault situation to be identified by means of the method foranomaly and/or fault recognition.

In particular, the fault cause is identifiable by means of the structuregraph of the anomaly and/or fault model.

Preferably, the structure graph for identifying the anomaly and/or faultsituation and/or for identifying the fault cause is displayed to a user.

The structure graph is configured to enable in particular a root causeanalysis. In particular, anomalous process values within a processstructure of the industrial-method plant are identifiable.

Preferably, a recognised anomaly is labellable by a user as a faultsituation or false alarm.

Fault situations are in particular stored in a fault database.

In one embodiment of the method for anomaly and/or fault recognition, itis provided for the industrial-method plant to comprise or to be formedby one or more of the following treatment stations of a painting plant:

-   -   pre-treatment station;    -   station for cathodic dip coating;    -   drying stations;    -   industrial supply air plant;    -   painting robot.

Further, the disclosure relates to an anomaly and/or fault recognitionsystem for recognising an anomaly and/or fault, which takes a form andis constructed to carry out the method according to examples disclosedherein for anomaly and/or fault recognition in an industrial-methodplant, for example a painting plant.

The anomaly and/or fault recognition system in particular forms amessage system by means of which a fault situation in theindustrial-method plant is recognisable in automated manner.

Further, the disclosure relates to an industrial control system thatcomprises an anomaly and/or fault recognition system according toexamples disclosed herein.

The method according to examples disclosed herein for anomaly and/orfault recognition preferably has individual or a plurality of thefeatures and/or advantages described in conjunction with the methodaccording to examples disclosed herein for fault analysis and/or themethod according to examples disclosed herein for predicting processdeviations.

The method according to examples disclosed herein for fault analysisand/or the method according to examples disclosed herein for predictingprocess deviations preferably have individual or a plurality of thefeatures and/or advantages described in conjunction with the methodaccording to examples disclosed herein for anomaly and/or faultrecognition.

Further features and/or advantages of examples disclosed herein form thesubject matter of the description below and the representation in thedrawing of exemplary embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic representation of an industrial-method plantand an industrial control system;

FIG. 2 shows a schematic representation of an industrial-method plant,in particular a painting plant;

FIG. 3 shows a schematic representation of an industrial supply airplant;

FIG. 4 shows the schematic representation of the industrial supply airplant from FIG. 3 on the occurrence of a fault situation;

FIG. 5 shows the schematic representation of the industrial supply airplant from FIG. 3 on the occurrence of a further fault situation;

FIG. 6 shows the schematic representation of the industrial supply airplant from FIG. 3 on the occurrence of a further fault situation;

FIG. 7 shows a further schematic representation of an industrial supplyair plant;

FIG. 8 shows the schematic representation of the industrial supply airplant from FIG. 7 in an operating state with no process deviation;

FIG. 9 shows the schematic representation of the industrial supply airplant from FIG. 7 in an operating state with a process deviation as aresult of changing ambient conditions;

FIG. 10 shows the schematic representation of the industrial supply airplant from FIG. 7 in an operating state with a process deviation as aresult of switching on a heat recovery system;

FIG. 11 shows the schematic representation of the industrial supply airplant from FIG. 7 in an operating state with a process deviationresulting from the failure of a valve;

FIG. 12 shows a schematic representation of process values that aregrouped into prediction data sets;

FIG. 13 shows a schematic representation of the prediction data setsfrom

FIG. 12, which are labelled as prediction data sets with processdeviations and prediction data sets with no process deviations;

FIG. 14 shows a schematic representation of a pre-treatment station;

FIG. 15 shows a schematic representation of method steps for generatingan anomaly and/or fault model of the pre-treatment station;

FIG. 16 shows a schematic representation of a graph having a processstructure derived from the pre-treatment station from FIG. 14;

FIG. 17 shows a clique of a factor graph;

FIG. 18 shows a model of a functional relationship in the clique fromFIG. 17; and

FIG. 19 shows a clique corresponding to the clique from FIG. 17, whichhas been expanded by one node by allocating a fault cause.

Like or functionally equivalent elements are provided with the samereference numerals in all the Figures.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an industrial control system that is designated 100 as awhole, for an industrial-method plant 101.

The industrial-method plant 101 is for example a painting plant 104,which is illustrated in particular in FIG. 2.

In particular a method for fault analysis in the industrial-method plant101, in particular the painting plant 102, is explained with referenceto FIGS. 1 to 6.

In particular a method for predicting process deviations in theindustrial-method plant 101, in particular the painting plant 102, isexplained with reference to FIGS. 1, 2 and 7 to 13.

In particular a method for anomaly and/or fault recognition in theindustrial-method plant 101, in particular the painting plant 102, isexplained with reference to FIGS. 1, 2 and 14 to 19.

The industrial-method plant 101, in particular the painting plant 102,that is illustrated in FIG. 2 preferably comprises a plurality oftreatment stations 104 for treating workpieces 106, in particular fortreating vehicle bodies 108.

In the embodiment of the painting plant 102 that is illustrated in FIG.2, the treatment stations 104 are in particular connected in a sequenceand so form a painting line 110.

For the purpose of treating workpieces 106, in particular for thepurpose of painting vehicle bodies 108, the workpieces 106 preferablypass through the treatment stations 104 one after the other.

For example, it is conceivable for a workpiece 106 to pass throughsuccessive treatment stations 104 in the order indicated.

A workpiece 106 is pre-treated in a pre-treatment station 112 andconveyed from the pre-treatment station 112 to a station for cathodicdip coating 114.

After the application of a coating to the workpiece 106, it is conveyedfrom the station for cathodic dip coating 114 to a drying station 116downstream of the station for cathodic dip coating 114.

After drying, in the drying station 116, of the coating that was appliedto the workpiece 106 in the station for cathodic dip coating 114, theworkpiece 106 is preferably conveyed to a base coat booth 118, in whichonce again a coating is applied to the workpiece 106.

After the application of the coating in the base coat booth 118, theworkpiece 106 is preferably conveyed to a base coat drying station 120.

After drying, in the base coat drying station 120, of the coating thatwas applied to the workpiece 106 in the base coat booth 118, theworkpiece 106 is preferably conveyed to a clear coat booth 122, in whicha further coating is applied to the workpiece 106.

After the application of the coating in the clear coat booth 122, theworkpiece 106 is preferably conveyed to a clear coat drying station 124.

After drying, in the clear coat drying station 124, of the coating thatwas applied to the workpiece 106 in the clear coat booth 122, theworkpiece 106 is preferably fed to an inspection station 126 at the endof the production process.

In the inspection station 126, a quality inspection is preferablycarried out by a quality inspector, for example by means of a visualinspection.

The industrial-method plant 101, in particular the painting plant 102,preferably further comprises an industrial supply air plant 128 forconditioning the air that is supplied for example to the base coat booth118 and/or the clear coat booth 122.

By means of the industrial supply air plant 128, a temperature and/orrelative air humidity of the air supplied to the base coat booth 118and/or the clear coat booth 122 is preferably adjustable.

By means of the industrial control system 100, preferably a productionprocess, in particular a painting process, is controllable in treatmentstations 104 of the industrial-method plant 101, in particular thepainting plant 102.

Preferably, for this purpose the industrial control system 100 comprisesa process checking system 130.

Further, the industrial control system 100 illustrated in FIG. 1preferably comprises a database 132.

The database 132 of the industrial control system 100 preferablycomprises a process database 134 and a fault database 136.

It may further be favourable if the industrial control system 100comprises a message system 138 and an analysis system 140.

Further, the industrial control system 100 preferably comprises adisplay system 142 by means of which information is displayable to auser.

Preferably here, the display system 142 comprises one or more screens onwhich information is presentable.

The analysis system 140 preferably comprises or is formed by a faultanalysis system 144.

It may further be favourable if the message system 138 comprises or isformed by a prediction system 146 for predicting process deviations inthe industrial-method plant 101.

As an alternative or in addition, it is conceivable for the messagesystem 138 to comprise an anomaly and/or fault recognition system 148.

The fault analysis system 144 in particular takes a form and isconstructed to carry out methods for fault analysis in theindustrial-method plant 101, which are explained with reference to FIGS.1 to 6.

The prediction system 146 in particular takes a form and is constructedto carry out the method for predicting process deviations in theindustrial-method plant 101, which are explained with reference to FIGS.1, 2 and 7 to 13.

The anomaly and/or fault recognition system 148 is in particularconstructed to carry out methods for anomaly and/or fault recognition inthe industrial-method plant 101, which are explained with reference toFIGS. 1, 2 and 14 to 19.

The industrial supply air plant 128 that is illustrated in FIGS. 3 to 6preferably comprises a plurality of conditioning modules 150, forexample a pre-heating module 154, a cooling module 156, a post-heatingmodule 158 and/or a wetting module 160.

For example, the industrial supply air plant 128 of the painting plant102 is a functional unit, wherein a conditioning module 150 of thesupply air plant 128 is a functional group and a circulation pump 152 ofthe supply air plant is a functional element (cf. FIGS. 3 to 6).

In addition to the circulation pumps 152 of the pre-heating module, thecooling module 156 and the post-heating module 158, the industrialsupply air plant 128 preferably further comprises a wetting pump 153 ofthe wetting module 160.

It may further be favourable if the supply air plant 128 comprises aventilator 162.

The supply air plant 128 preferably further comprises a heat recoverysystem 164 for the purpose of heat recovery.

Preferably, an air stream 165 is suppliable to the supply air plant 128from an area surrounding it.

An air stream 167 that is conditioned by means of the supply air plant128 is preferably suppliable to the base coat booth 118 and/or the clearcoat booth 122.

Preferably, the supply air plant comprises sensors (not represented inthe drawings of the Figures) by means of which process values aredetectable.

For example, detectable by means of the sensors are the followingprocess values, which are preferably respectively designated by means ofa reference numeral in FIGS. 3 to 6:

-   -   external temperature 166;    -   external humidity 168;    -   temperature of the air 170 conditioned by means of the        industrial supply air plant;    -   humidity of the air 172 conditioned by means of the industrial        supply air plant;    -   volumetric flow rates 174, 176, 178 in the conditioning modules        150;    -   valve positions 180, 182, 184 of valves 181, 183, 185 in the        conditioning modules 150.

Further, it may be favourable if a rotational frequency 193 of thewetting pump 153 and a rotational frequency 195 of the ventilator 194are detected.

Preferably, the process values 166 to 184 are stored in the processdatabase 134.

Further, it may be provided for the following status variables to bedetected, which are preferably likewise respectively designated by meansof a reference numeral in FIGS. 3 to 6:

-   -   pump status 186, 188, 190 of the circulation pumps 152 of the        conditioning modules 150 and pump status 192 of the wetting pump        153 (on/off);    -   valve status 194 (on/off);    -   valve status 196, 198, 200 of the conditioning modules 150        (open/closed);    -   status 202 of the heat recovery system 164 (on/off).

Preferably, the status variables 186 to 202 are also stored in theprocess database 134.

The method for fault analysis in the industrial-method plant 101 is nowexplained preferably with reference to FIGS. 3 to 6.

Here, the industrial supply air plant 128 in particular forms theindustrial-method plant 101.

Various exemplary situations are described below, from which functioningof the fault analysis system 144 can be seen.

Exemplary Situation 1 (cf. FIG. 4) Valve Leak

A valve leak occurs in the pre-heating module 154. A volumetric flowrate 174 of >0 is measured. The pump status 186 is “off” and the valvestatus 196 of the control valve is “closed”.

The fault situation is stored in the message system as an item of logic(pump status 186=off; volumetric flow rate 174>0; valve status 196closed). Thus, the message system has preferably stored the process andstatus values as a prior link.

The fault analysis steps on occurrence of a message as a result of thevalve leak are preferably the following:

1) The message is displayed to a user by means of the display system142, for example by means of a screen of the display system 142.

2) A user wishes to analyse the situation, and opens a diagnosticwindow.

3) The process value 174 that is linked to the message and the statusvariables 186 and 196 are displayed to the user in the diagnosticwindow. Preferably, the fault analysis system 144 receives thisinformation directly from the message system 138.

4) The process values linked to the fault situation are preferably notprioritised, since only the process value 174 is associated with thefault situation.

5) The user stores the fault situation, with the link, in the faultdatabase 136.

6) If the fault situation “valve leak” occurs again at the same or acomparable valve, the comparable fault situation is preferably displayedto the user.

Exemplary Situation 2 (cf. FIG. 5) Excessive Temperature 170 of the AirThat is Conditioned by Means of the Industrial Supply Air Plant 128, asa Result of too High an External Temperature 166

The temperature 170 of the air that is conditioned by means of theindustrial supply air plant 128 is too high, because the externaltemperature 166 is outside a design window of the industrial supply airplant 128.

When the temperature 170 of the air conditioned by means of theindustrial supply air plant 128 departs from a predetermined processwindow, the message is generated and the message system 138 sends it tothe display system 142.

The message is linked to the value of the temperature 170 of the airthat is conditioned by means of the industrial supply air plant 128.However, the message is not linked to the external temperature 166.

The fault analysis steps when the message arises as a result oftemperature change are preferably the following:

1) The message is displayed to a user by means of the display system142, for example by means of a screen of the display system 142.

2) A user wishes to analyse the situation, and opens a diagnosticwindow.

3) The process value 170 that is linked to the message is displayed tothe user in the diagnostic window.

4) The process values linked to the fault situation are preferably notprioritised, since only the process value 166 is associated with thefault situation.

5) The following process values are additionally preferably proposed tothe user:

-   -   humidity of the air 172 conditioned by means of the industrial        supply air plant (process-critical variable, displays an anomaly        in behaviour);    -   external temperature 166 (displays an anomaly in behaviour);    -   external humidity 168 (displays an anomaly in behaviour).

6) The user selects the proposed process values and adds them to thefault situation.

7) The user can capture the cause of the temperature deviation directlyfrom the analysis system 140, in particular from the fault analysissystem 144, since the relevant process values are proposed to the user.

8) The user adds documentation to the fault situation, with a proposalfor eliminating the fault.

9) The user stores the fault situation, with the link and the documents,in the fault database.

10) In addition to a fault ID, a fault classification (temperatureincrease), a fault location (exhaust part of the industrial supply airplant 128), the fault analysis system 144 preferably also capturesreferences (IDs) of the process variables 170, 172, 166, 168 in theprioritised order and a quantity of features (such as averages, minimum,maximum, scatter of the process variables during occurrence of the faultsituation) and the duration from occurrence of the fault situation untilthe point in time of storage or the end of the fault situation.

Exemplary Situation 3 (cf. FIG. 5) Excessive Temperature 170 of the AirThat is Conditioned by Means of the Industrial Supply Air Plant as aResult of Too High an External Temperature

The temperature 170 of the air that is conditioned by means of theindustrial supply air plant is again too high because of the externaltemperature. The fault pattern is similar to exemplary situation 2.

The analysis steps when the message arises as a result of temperaturechange are preferably the following:

1) The message is displayed to a user by means of the display system142, for example by means of a screen of the display system 142.

2) The user wishes to analyse the fault situation, and opens adiagnostic window.

3) The process values 170, 172, 166, 168 that are linked to the messageare displayed to the user in the diagnostic window, in the orderindicated.

4) A process list and its prioritisation are produced from a similarfault situation. The similarity to the fault situation from exemplarysituation 2 is determined by the fault analysis system 144 by a metricmatching of the process values.

5) The similar fault situation is displayed to the user.

6) The user can utilise the courses of the process values in theprioritised order shown, and the documents of the similar faultsituation displayed, in order to find a solution.

7) The user stores the fault situation.

Exemplary Situation 4 (cf. FIG. 6) Disruption in a Supply System

Too little combustion gas is supplied to a burner in the pre-heatingmodule 154. The volumetric flow rate 174 falls.

In order to receive more combustion gas, the valve 181 of thepre-heating module 154 is opened further and the valve position 180changes.

The valve 185 of the post-heating module 158 also opens in order tocompensate for the disruption in the pre-heating module 154. The valveposition 184 changes.

Because of the low external temperature 166, the disruption cannot becompensated, and the temperature 170 of the air conditioned by means ofthe industrial supply air plant plummets.

The analysis steps when the message arises as a result of the disruptionin the supply system are preferably the following:

1) The message is displayed to a user by means of the display system142, for example by means of a screen of the display system 142.

2) The user wishes to analyse the fault situation, and opens adiagnostic window.

3) The process value 170 that is linked to the message is displayed tothe user in the diagnostic window, as a result of prior prioritisationin the message system 138.

4) No prioritisation takes place.

5) The following process values are proposed:

-   -   humidity of the air 172 conditioned by means of the industrial        supply air plant (process-critical variable, displays an anomaly        in behaviour);    -   volumetric flow rate 174, valve position 180, valve position 186        (dependent on deviation from normal condition).

6) The fault situations from exemplary situations 2 and 3 are notclassified as similar (different signal behaviour because the metricdistance between the process values is high).

7) The proposed process values can be added to the fault situation andstored in the fault database.

The method for predicting process deviations in the industrial-methodplant 101 is now explained preferably with reference to FIGS. 7 to 13.

If the industrial-method plant 101 is an industrial supply air plant128, stored process values and/or status variables preferably comprisethe following (cf. FIG. 7):

-   -   target variables 204 of the industrial supply air plant 128, in        particular temperature 170 and relative air humidity 172 of the        air conditioned by means of the industrial supply air plant 128,        in particular at an exhaust part of the industrial supply air        plant 128;    -   control variables 206, in particular valve positions 180, 182,        184 of valves of heating and/or cooling modules 154, 156, 158 of        the industrial supply air plant 128, rotational frequencies 193        of pumps 152, in particular the wetting pump 153, and/or        rotational frequencies 195 of ventilators 162;    -   internal variables 208, in particular supply and/or return flow        temperatures 210 in the heating and/or cooling modules 154, 156,        158 of the industrial supply air plant 128 and/or air conditions        between conditioning modules 150;    -   measured disruption variables 210, in particular external        temperature 166 and/or external relative air humidity 168 at an        intake part of the industrial supply air plant 128;    -   unmeasured disruption variables 212; and/or    -   status variables 214, in particular wetting pump 153 (on/off);        manual mode for pumps 152, 153 (on/off); feed valves 181, 183,        185, ventilator 162 (on/off).

Various exemplary operating states are described below, from whichfunctioning of the prediction system 146 can be seen.

Exemplary Operating State 1 No Deviation

FIG. 8 shows a first exemplary operating state of the industrial supplyair plant 128 with no process deviation. Thus, the first exemplaryoperating state preferably represents a positive case.

The external temperature 166 and external humidity 168 are not constant.The pre-heating module 154 and wetting module 160 are active.

A control system of the industrial supply air plant 128 keeps thetemperature 170 and the relative air humidity 172 of air conditioned bymeans of the industrial supply air plant 128 at a constant value.

In accordance with the status variables 214 (ventilator 162=on and valveand pump mode=automatic), the industrial supply air plant 128 isoperation-ready.

Further, because of the constant temperature 170 and relative airhumidity 172 of the air that is conditioned by means of the industrialsupply air plant 128, the industrial supply air plant 128 is preferablyproduction-ready.

Exemplary Operating State 2 Increase in the External Temperature 166With Fall in the External Relative Humidity 168, Above the TechnicalParameters; Effect of a Measured Disruption Variable 210 on TargetVariable 204

FIG. 9 shows a second exemplary operating state of the industrial supplyair plant 128 with an increase in the external temperature 166 at thesame time as a fall in the external relative humidity 168 as a result ofa sudden change in the weather.

The process values display the following behaviour:

a) The increased external temperature 166 after the sudden change in theweather is outside the technical parameters.

b) The power of the previously active pre-heater module 154 is reducedby the controller.

c) Because the reduction in the heating power is not sufficient, thecontroller opens the valve 183 of the cooling module 156 and henceincreases the cooling power.

d) The rotational frequency 193 of the wetting pump 153 is increased bythe controller in order to compensate for the falling external humidity168.

e) Because, as a result of the insufficient design of the cooling module156, the cooling power is not sufficient, there is a deviation in thetemperature 170 of the air that is conditioned by means of theindustrial supply air plant 128.

A departure from the predetermined process window for the temperature170 is delayed as a result of the inertia of the industrial supply airplant 128 and compensation by the controller.

Exemplary Operating State 3 Changeover to Winter Operation With HeatRecovery; Effect of an Unmeasured Disruption Variable 212 on TargetVariable 204

FIG. 10 shows a third exemplary operating state of the industrial supplyair plant 128 when the heat recovery system 164, which uses waste heatto heat the air stream 165 in cold climatic conditions, is switched on.

The heat recovery system 164 is switched on by a manual valve, and forthis reason the effect of heat recovery by the heat recovery system 164is not measurable (unmeasured disruption variable 212).

The process values display the following behaviour:

a) The heating power of the heat recovery system 164 is increased; thevalue is not measurable.

b) The valve 181 of the pre-heating module 154 closes because of theincrease in heating power.

c) In the cooling module 156, the valve 183 opens in order to maintainthe temperature by additional cooling power.

d) The rotational frequency 193 of the wetting pump 153 is adapted bythe controller such that the air humidity 172 of the air conditioned bymeans of the industrial supply air plant 128 is maintained.

e) There is a deviation in the temperature 170 of the air conditioned bymeans of the industrial supply air plant 128 because the cooling module156 cannot compensate for the heat supply quickly enough. Because of theinertia of the industrial supply air plant 128 and compensation by thecontroller, the deviation occurs with a delay.

Exemplary Operating State 4 Failure of the Valve 181 of the Pre-HeatingModule 154

FIG. 11 shows a fourth exemplary operating state of the industrialsupply air plant 128 when there is a failure of the valve 181 of thepre-heating module 154.

The process values display the following behaviour:

a) The valve 181 of the pre-heating module 154 closes because of adevice fault, as a result of which the heating power falls.

b) The valve 185 of the post-heating module 158 opens in order tocompensate for the lacking heating power.

c) Because the heating power of the post-heating module 158 is notsufficient, there is a deviation in the temperature 170 of the airconditioned by means of the industrial supply air plant 128.

The method for predicting process deviations in the industrial-methodplant 101, in particular in the industrial supply air plant, isexplained below with reference to the operating states 1 to 4 describedabove.

Preferably, the operating states 2 to 4 with process deviations duringoperation of the industrial supply air plant 128 are predictable bymeans of the method for predicting process deviations with a predictionhorizon 216 of for example approximately 15 minutes.

As the data basis for training a prediction model, a timespan isconsidered in which the industrial supply air plant 128 runs in normaloperation (>80%) in an operating state that is ready for use (cf.exemplary operating state 1).

The recorded data contain the exemplary operating states 2 to 4,preferably in each case multiple times. These may have occurred inongoing operation, or as an alternative may have been brought aboutdeliberately, for example by closing a valve 181, 183, 185.

The data are preferably then pre-processed and regularised, as can beseen for example from FIG. 12. There, the valve position 180 of thevalve 181 of the pre-heating module 154 and the temperature 170 andrelative air humidity 172 of the air conditioned by means of theindustrial supply air plant 128 are recorded as examples.

The regularised data are divided for example into time windows 218 of 30minutes, in each case with a time offset of for example 5 minutes.

The data regularised into time windows 218 in particular form predictiondata sets, in particular prediction data sets with no process deviations220 and prediction data sets with a process deviation 222 (cf. FIG. 7).

For the prediction data sets with a process deviation 222, the statusvariables 214 are used to check whether the industrial-method plant 101was operation-ready (for example, ventilator 162 on, conditioningmodules 150 in automatic mode).

-   -   If not: corresponding prediction data sets with a process        deviation 222 are rejected and are not used for training the        prediction model.    -   If it was: corresponding prediction data sets with a process        deviation 222 are contenders for training the prediction model.

Because of a minimum time interval of for example one hour, in FIG. 7only one prediction data set with a process deviation 222 is selected.

Preferably, selection of the prediction data sets with no processdeviations 220 is performed analogously to selection of the predictiondata sets with a process deviation 222.

With a minimum time interval of for example one hour, only oneprediction data set with no process deviations 220 is selected fortraining the prediction model.

Then, features are preferably extracted from the selected predictiondata sets with no process deviations 220 and the selected predictiondata sets with process deviations 222.

For the purpose of extracting the features, there are used for examplestatistical key figures, for example minimum, maximum, median, averageand/or standard deviation. It may further be favourable if linearregression coefficients are used for extracting the features.

Preferably, the prediction model is trained on the basis of theextracted features from the selected prediction data sets with noprocess deviations 220 and on the basis of the selected prediction datasets with process deviations 222, in particular by means of a machinelearning method, for example by means of gradient boosting.

Using the trained prediction model, process deviations ofproduction-critical process values in the industrial supply air plant128 are preferably predicted on the basis of changing process valuesduring operation of the industrial supply air plant 128.

In particular, the prediction model is explained with reference toexemplary operating states 2 to 4:

Exemplary Operating State 2

The prediction model predicts a process deviation after the occurrenceof an increase in temperature. The basis for this is the measureddisruption variables 210, in particular external temperature 166 andexternal humidity 168, the response of the conditioning modules 150, andthe course of the temperature 170 of the air conditioned by means of theindustrial supply air plant 128 at the exhaust part.

Exemplary Operating State 3

The prediction model predicts an increase, after the heat recoverysystem is switched on, in the temperature 170 of the air conditioned bymeans of the industrial supply air plant 128. The basis is the responseof the conditioning modules 150 and the course of the temperature 170 ofthe air conditioned by means of the industrial supply air plant 128 atthe exhaust part.

Exemplary Operating State 4

The prediction model predicts an increase in the temperature 170 of theair conditioned by means of the industrial supply air plant 128, on thebasis of the weather conditions and the valve position 180 of the valve181 of the pre-heating module 156.

The method for anomaly and/or fault recognition in the industrial-methodplant 101 is now explained preferably with reference to FIGS. 14 to 19.

Fault situations, in particular defects and/or failures in components,sensors and/or actuators, are preferably identifiable by means of themethod for anomaly and/or fault recognition.

Here, the pre-treatment station 112 for example forms theindustrial-method plant 101.

Preferably, the pre-treatment station 112 comprises a pre-treatment tank224 in which workpieces 106, preferably vehicle bodies 108, arepre-treatable.

Preferably, the pre-treatment station 112 further comprises a first pump226, a second pump 228, a heat exchanger 230 and a valve 232.

The process values V62dot, S86, T95, T85, T15 and T05 are given theirdesignation on the basis of an unambiguous designation in a numberingsystem of the industrial-method plant 101.

The process values T95, T85, T15 and T05 represent in particulartemperatures within the industrial-method plant 101, in particularwithin the pre-treatment station 112.

The process value S86 is a valve position of the valve 232.

The process value V62dot is a volumetric flow rate.

Preferably, for the purpose of carrying out the method for anomalyand/or fault recognition, an anomaly and/or fault model 233 of theindustrial-method plant 101, in particular the pre-treatment station112, is generated, comprising information on the occurrence probabilityof the above-mentioned process values (cf. FIG. 15).

The anomaly and/or fault model 233 is preferably generated as follows:

First, test signals are generated, in particular taking into accounttechnical data 234 in the context of test signal generation 236.

In particular, limits for the test signals are predetermined on thebasis of the technical data 234, for example, when predetermining jumpfunctions, a maximum amplitude for control variable jumps.

The technical data 234 comprise for example one or more of the followingitems of information:

-   -   sensor type (temperature sensor, throughflow sensor, valve        position, pressure sensor, etc.) and/or actuator type (valve,        ventilator, damper, electric motor);    -   permissible value ranges of sensors and/or actuators;    -   signal type of sensor and/or actuator (float, integer).

The industrial-method plant 101, in particular the pre-treatment station112, is preferably activated dynamically by means of the test signals.This is indicated in FIG. 15 by the reference numeral 238. Here, it isconceivable for anomalies and/or fault situations to be generateddeliberately on activation with test signals.

During activation of the industrial-method plant 101, in particular thepre-treatment station 112, by test signals, preferably system inputsignals 240 and system output signals 242 are generated.

The system input signals 240 and system output signals 242 arepreferably stored in a test signal database 244.

Then, preferably a structure identification 246 of the industrial-methodplant 101, in particular the pre-treatment station 112, is carried out.Here, preferably a structure graph 247 of the industrial-method plant101, in particular the pre-treatment station 112, is determined (cf.FIG. 16).

The structure identification 246, in particular determination of thestructure graph, is preferably performed using a machine learningmethod, preferably using correlation coefficients by means of whichnon-linear relationships are reproducible, for example by means ofmutual information.

It may further be favourable if, for the purpose of structureidentification 246, expert knowledge 248 is used, that is to say inparticular knowledge of relationships in the process.

Here, for example edges between nodes of the structure graph that is tobe determined can be eliminated by a pre-configuration of the structuregraph, by means of information from expert knowledge, known circuitdiagrams and/or procedure diagrams 250. In particular here, processingwork for determining the structure graph is reducible.

It may further be favourable if the structure graph is determined usingthe respectively unambiguous designation of the process values by way ofa numbering system of the industrial-method plant 101, in particular thepre-treatment station 112, that is to say using semantics 252 of thedesignation of the process values.

In particular, it is conceivable for the structure graph that isdetermined by means of the machine learning method to be checked forplausibility by means of expert knowledge 248, known circuit diagramsand/or procedure diagrams 250 and/or the designations in the numberingsystem of the industrial-method plant 101 (semantics 252).

Preferably, causalities 254 in the determined process structure are thendetermined, in particular directions marked by arrows in the determinedstructure graph.

Causalities 254 in the determined process structure are derived forexample from system input signals 240 and system output signals 242 ofthe industrial-method plant 101 that are determined during activation ofthe industrial-method plant 101 by test signals, for example by way ofthe respective temporal course of the system input signals 240 andsystem output signals 242.

As an alternative or in addition, it is conceivable for causalities 254to be derived from system input signals 240 and system output signals242 that are determined during activation of the industrial-method plant101 by test signals, by means of causal inference methods.

For determining the causalities 254, there are further preferably usedexpert knowledge 248, procedure diagrams 250 and/or the designations inthe numbering system of the industrial-method plant 101 (semantics 252).

Preferably, the process values that cause a recognised anomaly arelocatable by means of the causalities 254 determined in the determinedprocess structure or in the determined structure graph.

After the structure identification 246 and/or determination of thecausalities 254, preferably a structure parameterisation 256 is carriedout.

Preferably, the structure identification 246 is configured to facilitatestructure parameterisation 256. Preferably, the structure identification246 is configured to reduce processing work for the structureparameterisation 256.

Preferably, the structure parameterisation 256 is performed using amethod for determining probability density functions, in particularusing Gaussian mixture models.

The structure parameterisation 256 is carried out for example for thecommon probability density function f1 of the clique 258 represented inFIG. 17 using Gaussian mixture models (cf. FIG. 18).

Preferably, expert knowledge 248 is likewise used for the structureparameterisation 256.

For the purpose of structure parameterisation 256, for example knownphysical relationships between process values and/or physicalcharacteristic diagrams of functional elements of the industrial-methodplant 101, in particular the pre-treatment station, are used. Forexample, a characteristic diagram of the valve 232 is used.

It may further be favourable if expert knowledge 248 on fault situationsis used for structure parameterisation 256.

For example, a relationship between the valve position S86 and thevolumetric flow rate V62dot is describable by means of a known valvecharacteristic diagram of the valve 232.

Data that are stored in an operations database 260 from regularoperation of the industrial-method plant 101, in particular thepre-treatment station 112, and/or data from the test signal database 244are preferably used for the purpose of structure parameterisation 256using methods for determining probability density functions, inparticular using Gaussian mixture models.

For example, control, measurement and/or regulating variables that arestored in particular in a database 244, 260 are used for the purpose ofstructure parameterisation 256 using methods for determining probabilitydensity functions.

Preferably, for the purpose of structure parameterisation 256 usingmethods for determining probability density functions, data from ongoingoperation of the industrial-method plant 101, in particular thepre-treatment station, are used, and these are stored for a period of atleast 2 weeks, preferably at least 4 weeks, for example at least 8weeks.

The data are preferably pre-processed before the structureparameterisation 256.

During the pre-processing, preferably data from the industrial-methodplant 101 that are not associated with operation-ready orproduction-ready operating states of the industrial-method plant 101(for example plant switched off, maintenance phases, etc.) areeliminated in particular by way of alarms and status bits that describethe state of the industrial-method plant 101, in particular thepre-treatment station 112.

Further, it may be favourable if data from the industrial-method plant101 are pre-processed by filtering, for example by means of low-passfilters and/or Butterworth filters.

Preferably, the data are further interpolated at a consistent timeinterval.

During generation of the anomaly and/or fault model 233, a limit valuefor the occurrence probability of a process value is preferablyestablished in the context of a limit value optimisation 264.

The limit value for the occurrence probability is preferably establishedsuch that if this falls below the limit value an anomaly is recognised.

The limit value is preferably established by means of a non-linearoptimisation method, for example by means of the Nelder-Mead method.

As an alternative or in addition, it is conceivable to establish thelimit value by means of quantiles.

Limit values for the occurrence probability of the process values arepreferably optimisable, for example by predetermining a false-positiverate.

Further, it is conceivable for the limit values to be adapted after thefirst generation of the anomaly and/or fault model 233, in particular inthe event of too high a number of false alarms.

Preferably, anomaly and/or fault recognition is performed using theanomaly and/or fault model 233 as follows:

For example, the valve 232 undergoes valve failure and thus the sensorvalues deviate from the mapped normal condition in the individualcliques.

The occurrence probabilities of the sensor values in the cliques areevaluated during operation of the industrial-method plant 101, inparticular of the pre-treatment station 112, and if they fall below thecalculated limit values anomalies are detected in the different cliques.

Valve failure of the valve 232 results initially in an anomaly in theclique 258 of the valve position S86, wherein a message is output by theanomaly and/or fault recognition system 148.

As time continues, as a result of fault propagation further anomaliesare produced, which later also affect the process-relevant variable, forexample the tank temperature T35 of the tank 224.

Preferably, the message from the anomaly and/or fault recognition system148 contains one or more of the following items of information:

-   -   point in time at which the anomaly was detected;    -   clique(s) in which the anomaly occurred, with sensors affected.

As a result of early recognition of the anomaly and the message to theuser, with prompt intervention it is preferably possible to preventdeviation of the process-relevant variable, that is to say the tanktemperature T35 of the tank 224.

The user can then define a cause of the fault (that is to say the valvefailure) for occurrence of the anomaly.

As a result of allocating the fault cause, the clique 258 is expanded byone node 266 and the probability density function of the anomalous datais integrated into the functional relationship (cf. FIG. 19).

After integration of the fault cause, the method for anomaly and/orfault recognition is carried out as before. If an anomaly occurs, theprobabilities of the defined fault causes are additionally output.

As a result of the message from the anomaly and/or fault recognitionsystem 148, a user now receives one or more of the following items ofinformation:

-   -   point in time at which the anomaly was detected;    -   clique(s) in which the anomaly occurred, with sensors affected;    -   probabilities of the defined fault causes.

Particular embodiments are the following:

Embodiment 1

A method for fault analysis in an industrial-method plant (101), forexample a painting plant (102), wherein the method comprises thefollowing:

-   -   in particular automatic recognition of a fault situation in the        industrial-method plant (101);    -   storage of a fault situation data set for the respective        recognised fault situation, in a fault database (136);    -   automatic determination of a cause of the fault for the fault        situation and/or automatic determination of process values that        are relevant to the fault situation, on the basis of the fault        data set of a respective recognised fault situation.

Embodiment 2

A method according to embodiment 1, characterised in that, for thepurpose of automatically determining the fault cause for the faultsituation and/or automatically determining the process values relevantto the fault situation, one or more process values are automaticallylinked to the fault situation on the basis of one or more of thefollowing link criteria:

-   -   prior linking from a message system;    -   an association of a process value with the same part of the        industrial-method plant (101) as that in which the fault        situation occurred;    -   linking a process value to a historical fault situation on the        basis of active selection by a user;    -   an active selection of the process value by a user.

Embodiment 3

A method according to embodiment 2, characterised in that, for thepurpose of automatically determining the fault cause for the faultsituation and/or automatically determining the process values relevantto the fault situation, automatic prioritisation of the process valueslinked to the fault situation is carried out automatically on the basisof one or more of the following prioritisation criteria:

-   -   a process relevance of the process values;    -   a position of a process value or of a sensor determining the        process value within the industrial-method plant (101);    -   an amount by which a process value deviates from a defined        process window and/or a normal condition;    -   a prioritisation of historical process values in historical        fault situations;    -   by adopting a prioritisation of the fault cause and/or the        process values from a message system (138); a prioritisation by        a user.

Embodiment 4

A method according to one of embodiments 1 to 3, characterised in that,for the purpose of automatically determining the fault cause for thefault situation and/or automatically determining the process valuesrelevant to the fault situation, further fault causes and/or processvalues are proposed, wherein the proposal is made automatically on thebasis of one or more of the following proposal criteria:

-   -   a process relevance of the process values;    -   a position of a process value or of a sensor determining the        process value within the industrial-method plant (101);    -   an amount by which a process value deviates from a defined        process window and/or a normal condition;    -   a prioritisation of historical process values in historical        fault situations;    -   physical dependences of the process values.

Embodiment 5

A method according to one of embodiments 1 to 4, characterised in thathistorical fault situations are determined from a fault database (136)using one or more of the following similarity criteria:

-   -   a fault classification of the historical fault situation;    -   a historical fault situation in the same or a comparable plant        part;    -   process values of the historical fault situation that are        identical or similar to process values of the recognised fault        situation.

Embodiment 6

A method according to one of embodiments 1 to 5, characterised in thathistorical process values that are identical or similar to processvalues of the recognised fault situation are determined from a processdatabase (134).

Embodiment 7

A method according to embodiment 6, characterised in that the determinedhistorical process values are characterised as belonging to a historicalfault situation.

Embodiment 8

A method according to one of embodiments 1 to 7, characterised in that,for a recognised fault situation, a fault situation data set is storedin a fault database (136).

Embodiment 9

A method according to embodiment 8, characterised in that a respectivefault identification data set comprises one or more of the followingfault situation data:

-   -   a fault classification of the fault situation;    -   process values that are linked to the fault situation, based on        a prior linking from a message system;    -   information on a point in time at which a respective fault        situation occurred;    -   information on a duration for which a respective fault situation        occurred;    -   information on the location in which a respective fault        situation occurred;    -   alarms;    -   status messages.

Embodiment 10

A method according to embodiment 8 or 9, characterised in that the faultsituation data set of a respective fault situation comprises faultidentification data for unambiguous identification of the recognisedfault situation.

Embodiment 11

A method according to one of embodiments 8 to 10, characterised in thatdocumentation data and fault elimination data are stored in the faultsituation data set of a respective fault situation.

Embodiment 12

A method according to one of embodiments 8 to 11, characterised in thatprocess values are stored during operation of the industrial-methodplant (101), synchronised with a recognised fault situation.

Embodiment 13

A method according to one of embodiments 8 to 12, characterised in thatprocess values are provided with a time stamp by means of which theprocess values are configured to be unambiguously associated with apoint in time.

Embodiment 14

A fault analysis system (144) for fault analysis in an industrial-methodplant (101), for example a painting plant (102), wherein the systemtakes a form and is constructed for the purpose of carrying out themethod for fault analysis in an industrial-method plant (101), forexample a painting plant (102), according to one of embodiments 1 to 13.

Embodiment 15

An industrial control system (100) that comprises a fault analysissystem (144) according to embodiment 14.

Embodiment 16

A method for predicting process deviations in an industrial-method plant(101), for example a painting plant (102), wherein the method comprisesthe following:

-   -   automatic generation of a prediction model;    -   prediction of process deviations during operation of the        industrial-method plant (101), using the prediction model.

Embodiment 17

A method according to embodiment 16, characterised in that the methodfor predicting process deviations is carried out in an industrial supplyair plant (128), a pre-treatment station (112), a station for cathodicdip coating (114) and/or a drying station (116, 120, 124).

Embodiment 18

A method according to embodiment 16 or 17, characterised in that processdeviations of production-critical process values in theindustrial-method plant (101) are predicted by means of the predictionmodel, on the basis of changing process values during operation of theindustrial-method plant (101).

Embodiment 19

A method according to one of embodiments 16 to 18, characterised inthat, for the purpose of automatically generating the prediction model,process values and/or status variables are stored during operation ofthe industrial-method plant (101) for a predetermined period.

Embodiment 20

A method according to embodiment 19, characterised in that thepredetermined period for which process values and/or status variablesare stored during operation of the industrial-method plant (101) ispredetermined in dependence on one or more of the following criteria:

-   -   the industrial-method plant (101) is in an operation-ready        state, in particular for a production operation, for at least        approximately 60%, preferably for at least approximately 80%, of        the predetermined period;    -   the industrial-method plant (101) is in a production-ready state        for at least approximately 60%, preferably for at least        approximately 80%, of the predetermined period;    -   during the predetermined period, the industrial-method plant        (101) is operated in particular using all possible operating        strategies;    -   a predetermined number of process deviations and/or disruptions        in the predetermined period.

Embodiment 21

A method according to embodiment 19 or 20, characterised in that, forthe purpose of generating the prediction model, a machine learningmethod is carried out, wherein the process values and/or statusvariables that are stored for the predetermined period are used forgenerating the prediction model.

Embodiment 22

A method according to embodiment 21, characterised in that the machinelearning method is carried out on the basis of features that areextracted from the process values and/or status variables stored for thepredetermined period.

Embodiment 23

A method according to embodiment 22, characterised in that one or moreof the following is used for the purpose of extracting features:

-   -   statistical key figures;    -   coefficients from a principal component analysis;    -   linear regression coefficients;    -   dominant frequencies and/or amplitudes from the Fourier        spectrum.

Embodiment 24

A method according to one of embodiments 16 to 23, characterised in thata selected number of prediction data sets with process deviations (222)and a selected number of prediction data sets with no process deviations(220) are used for training the prediction model.

Embodiment 25

A method according to embodiment 24, characterised in that selection ofthe number of prediction data sets with a process deviation is made onthe basis of one or more of the following criteria:

-   -   a minimum time interval between two prediction data sets with        process deviations;    -   an automatic selection on the basis of defined rules;    -   a selection by a user.

Embodiment 26

A method according to embodiment 24 or 25, characterised in thatprediction data sets with process deviations are characterised as suchif a process deviation occurs within a predetermined time interval.

Embodiment 27

A method according to embodiment 26, characterised in that the processvalues and/or status variables that are stored for the predeterminedperiod are grouped into prediction data sets by pre-processing.

Embodiment 28

A method according to embodiment 27, characterised in that thepre-processing comprises the following:

-   -   regularisation of the process values stored for the        predetermined period;    -   grouping the process values and/or status variables into        prediction data sets by classifying the process values and/or        status variables into time windows with a time offset.

Embodiment 29

A prediction system (146) for predicting process deviations in anindustrial-method plant, wherein the prediction system takes a form andis constructed for the purpose of carrying out the method for predictingprocess deviations in an industrial-method plant (101), for example apainting plant (102), according to one of embodiments 16 to 29.

Embodiment 30

An industrial control system (100) that comprises a prediction system(146) according to embodiment 29.

Embodiment 31

A method for anomaly and/or fault recognition in an industrial-methodplant (101), for example a painting plant (102), wherein the methodcomprises the following:

-   -   automatic generation of an anomaly and/or fault model (233) of        the industrial-method plant (101) that comprises information on        the occurrence probability of process values;    -   automatic input of process values of the industrial-method plant        (101) during operation thereof;    -   automatic recognition of an anomaly and/or fault situation by        determining an occurrence probability by means of the anomaly        and/or fault model (233) on the basis of the process values of        the industrial-method plant (101) that have been input and by        checking the occurrence probability for a limit value.

Embodiment 32

A method according to embodiment 31, characterised in that

-   -   the anomaly and/or fault model (233) comprises structural data        containing information on a process structure in the        industrial-method plant (101), and/or in that    -   the anomaly and/or fault model (233) comprises parameterisation        data containing information on relationships between process        values of the industrial-method plant (101).

Embodiment 33

A method according to embodiment 31 or 32, characterised in that, forthe purpose of generating the anomaly and/or fault model (233), one ormore of the following steps is carried out:

-   -   structure identification (246) for determining a process        structure of the industrial-method plant (101);    -   determination of causalities (254) in the determined process        structure of the industrial-method plant (101);    -   structure parameterisation (256) of the relationships in the        determined process structure of the industrial-method plant        (101).

Embodiment 34

A method according to embodiment 33, characterised in that, in thecontext of structure identification (246) for determining a processstructure of the industrial-method plant (101), a structure graph thatin particular maps relationships in the industrial-method plant (101) isdetermined.

Embodiment 35

A method according to embodiment 34, characterised in that determinationof the structure graph is performed using one or more of the following:

-   -   a machine learning method;    -   expert knowledge (248);    -   known circuit diagrams and/or procedure diagrams (250);    -   designations in a numbering system of the industrial-method        plant (101).

Embodiment 36

A method according to embodiment 33 to 35, characterised in that theindustrial-method plant (101) is activated by test signals for thepurpose of structure identification, in particular for determining thestructure graph.

Embodiment 37

A method according to one of embodiments 33 to 36, characterised in thatthe determining of causalities (254) in the determined process structureof the industrial-method plant (101) is performed using one or more ofthe following:

-   -   system input signals (240) and system output signals (242) that        are generated on activation of the industrial-method plant (101)        by test signals;    -   expert knowledge (248);    -   known circuit diagrams and/or procedure diagrams (252);    -   designations in a numbering system of the industrial-method        plant (101).

Embodiment 38

A method according to one of embodiments 33 to 37, characterised inthat, for the purpose of structure parameterisation (246) of therelationships in the determined process structure of theindustrial-method plant (101), one or more of the following is used:

-   -   methods for determining probability density functions, in        particular Gaussian mixture models;    -   known physical relationships between process values;    -   physical characteristic diagrams of functional elements of the        industrial-method plant (101), for example characteristic        diagrams of valves (232).

Embodiment 39

A method according to embodiment 38, characterised in that data fromregular operation of the industrial-method plant (101) and/or dataobtained by activation of the industrial-method plant (101) by testsignals are used for the purpose of structure parameterisation (246)using methods for determining probability density functions, inparticular using Gaussian mixture models.

Embodiment 40

A method according to embodiment 39, characterised in that the data thatare used for structure parameterisation (246) using methods fordetermining probability density functions, in particular using Gaussianmixture models, are pre-processed before the structure parameterisation(246).

Embodiment 41

A method according to one of embodiments 31 to 40, characterised in thatduring generation of the anomaly and/or fault model (233) a limit valuefor the occurrence probability of a process value is established,wherein an anomaly is recognised if this falls below the limit value.

Embodiment 42

A method according to one of embodiments 31 to 41, characterised in thata fault cause of a recognised anomaly and/or a recognised faultsituation is identified by means of the method for anomaly and/or faultrecognition.

Embodiment 43

A method according to one of embodiments 31 to 42, characterised in thatthe industrial-method plant (101) comprises or is formed by one or moreof the following treatment stations (104) of a painting plant:

-   -   pre-treatment station (112);    -   station for cathodic dip coating (114);    -   drying stations (116, 120, 124);    -   industrial supply air plant (128);    -   painting robot.

Embodiment 44

An anomaly and/or fault recognition system (148) for recognising ananomaly and/or fault, which takes a form and is constructed to carry outthe method for anomaly and/or fault recognition in an industrial-methodplant (101), for example a painting plant (102), according to one ofembodiments 31 to 43.

Embodiment 45

An industrial control system (100) that comprises an anomaly and/orfault recognition system (148) according to embodiment 44.

Embodiment 46

An industrial control system that comprises a fault analysis systemaccording to embodiment 14, a prediction system for predicting processdeviations in an industrial-method plant according to embodiment 29and/or an anomaly and/or fault recognition system according toembodiment 44.

1. A method for anomaly and/or fault recognition in an industrial-methodplant, wherein the method comprises the following: automaticallygenerating an anomaly and/or fault model of the industrial-method plantthat includes information on the occurrence probability of processvalues; automatically inputting process values of the industrial-methodplant during operation thereof; and automatically recognizing an anomalyand/or fault situation by determining an occurrence probability by meansof the anomaly and/or fault model on the basis of the process values ofthe industrial-method plant that have been input and by checking theoccurrence probability for a limit value.
 2. A method according to claim1, wherein the anomaly and/or fault model includes structural datacontaining information on a process structure in the industrial-methodplant, and/or in that the anomaly and/or fault model includesparameterisation data containing information on relationships betweenprocess values of the industrial-method plant.
 3. A method according toclaim 1, wherein, for the purpose of generating the anomaly and/or faultmodel, one or more of the following steps is carried out: structureidentification for determining a process structure of theindustrial-method plant; determination of causalities in the determinedprocess structure of the industrial-method plant; structureparameterisation of the relationships in the determined processstructure of the industrial-method plant.
 4. A method according to claim3, wherein, in the context of structure identification for determining aprocess structure of the industrial-method plant a structure graph thatin particular maps relationships in the industrial-method plant isdetermined.
 5. A method according to claim 4, wherein determination ofthe structure graph is performed using one or more of the following: amachine learning method; expert knowledge; known circuit diagrams and/orprocedure diagrams; designations in a numbering system of theindustrial-method plant.
 6. A method according to claim 3, wherein theindustrial-method plant is activated by test signals for the purpose ofstructure identification, in particular for determining the structuregraph.
 7. A method according to claim 3, wherein the determining ofcausalities in the determined process structure of the industrial-methodplant is performed using one or more of the following: system inputsignals and system output signals that are generated on activation ofthe industrial-method plant by test signals; expert knowledge; knowncircuit diagrams and/or procedure diagrams; designations in a numberingsystem of the industrial-method plant.
 8. A method according to claim 3,wherein, for the purpose of structure parameterisation of therelationships in the determined process structure of theindustrial-method plant, one or more of the following is used: methodsfor determining probability density functions, in particular Gaussianmixture models; known physical relationships between process values;physical characteristic diagrams of functional elements of theindustrial-method plant, for example characteristic diagrams of valves.9. A method according to claim 8, wherein data from regular operation ofthe industrial-method plant and/or data obtained by activation of theindustrial-method plant by test signals are used for the purpose ofstructure parameterisation using methods for determining probabilitydensity functions, in particular using Gaussian mixture models.
 10. Amethod according to claim 9, wherein the data that are used forstructure parameterisation using methods for determining probabilitydensity functions, in particular using Gaussian mixture models, arepre-processed before the structure parameterisation.
 11. A methodaccording to claim 1, wherein during generation of the anomaly and/orfault model a limit value for the occurrence probability of a processvalue is established, and wherein an anomaly is recognised if this fallsbelow the limit value.
 12. A method according to claim 1, wherein afault cause of a recognised anomaly and/or a recognised fault situationis identified by the method for anomaly and/or fault recognition.
 13. Amethod according to claim 1, wherein the industrial-method plantincludes or is formed by one or more of the following treatment stationsof a painting plant: pre-treatment station; station for cathodic dipcoating; drying stations; industrial supply air plant; and paintingrobot.
 14. An anomaly and/or fault recognition system for recognising ananomaly and/or fault, which takes a form and is constructed to carry outthe method for anomaly and/or fault recognition in an industrial-methodplant, for example a painting plant, according to claim
 1. 15. Anindustrial control system that comprises an anomaly and/or faultrecognition system according to claim 14.